1k citations
- Xilinx (United States)US5 papers
- Norwegian University of Science and TechnologyNO4 papers
- Fermi National Accelerator LaboratoryUS2 papers
- Northwestern UniversityUS2 papers
- The University of SydneyAU2 papers
- University of California, BerkeleyUS2 papers
- University of California San DiegoUS2 papers
- Association for Computing MachineryUS1 paper
- Birla Institute of Technology and Science, PilaniIN1 paper
- Cerebras Systems (United States)1 paper
- Coburg University of Applied SciencesDE1 paper
- Columbia UniversityUS1 paper
13 papers
Applications and Techniques for Fast Machine Learning in Science
Allison McCarn Deiana, Nhan Tran, Joshua Agar +84
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time…
Ps and Qs: Quantization-aware pruning for efficient low latency neural network inference
Benjamin Hawks, Javier Duarte, Nicholas J. Fraser +3
Efficient machine learning implementations optimized for inference in hardware have wide-ranging benefits, depending on the application, from lower inference latency to higher data…
LogicNets: Co-Designed Neural Networks and Circuits for Extreme-Throughput Applications
Yaman Umuroglu, Yash Akhauri, Nicholas J. Fraser +1
Deployment of deep neural networks for applications that require very high throughput or extremely low latency is a severe computational challenge, further exacerbated by inefficie…
Evolutionary Bin Packing for Memory-Efficient Dataflow Inference Acceleration on FPGA
Mairin Kroes, Lucian Petrica, Sorin Cotofana +1
Convolutional neural network (CNN) dataflow inference accelerators implemented in Field Programmable Gate Arrays (FPGAs) have demonstrated increased energy efficiency and lower lat…
Efficient Error-Tolerant Quantized Neural Network Accelerators
Giulio Gambardella, Johannes Kappauf, Michaela Blott +4
Neural Networks are currently one of the most widely deployed machine learning algorithms. In particular, Convolutional Neural Networks (CNNs), are gaining popularity and are evalu…
QuTiBench: Benchmarking Neural Networks on Heterogeneous Hardware
Michaela Blott, Lisa Halder, Miriam Leeser +1
Neural Networks have become one of the most successful universal machine learning algorithms. They play a key role in enabling machine vision and speech recognition for example. Th…